An Overview of Interrater Agreement on Likert Scales for Researchers and Practitioners

作者：O' Neill Thomas A

来源：Frontiers in Psychology, 2017, 8: 777.

DOI：10.3389/fpsyg.2017.00777

• interrater agreement
• rwg
• multilevel methods
• data aggregation
• within-group agreement
• reliability

Applications of interrater agreement (IRA) statistics for Likert scales are plentiful in research and practice. IRA may be implicated in job analysis, performance appraisal, panel interviews, and any other approach to gathering systematic observations. Any rating system involving subject-matter experts can also benefit from IRA as a measure of consensus. Further, IRA is fundamental to aggregation in multilevel research, which is becoming increasingly common in order to address nesting. Although, several technical descriptions of a few specific IRA statistics exist, this paper aims to provide a tractable orientation to common IRA indices to support application. The introductory overview is written with the intent of facilitating contrasts among IRA statistics by critically reviewing equations, interpretations, strengths, and weaknesses. Statistics considered include r(wg), r*(wg), r'(wg), r(wg(p)), average deviation (AD), a(wg), standard deviation (S-wg), and the coefficient of variation (CVwg). Equations support quick calculation and contrasting of different agreement indices. The article also includes a "quick reference" table and three figures in order to help readers identify how IRA statistics differ and how interpretations of IRA will depend strongly on the statistic employed. A brief consideration of recommended practices involving statistical and practical cutoff standards is presented, and conclusions are offered in light of the current literature.

• 出版日期2017-5-12